About

The Center for Data Science and Artificial Intelligence was established on January 1, 2015 with a mission to foster data science and artificial intelligence research, education, industry collaboration, and public service in ways that drive UMass Amherst to become recognized as a national leader in this scientific area. Situated within the Robert and Donna Manning College of Information and Computer Sciences (CICS), the Center for Data Science and Artificial Intelligence is charged with helping Massachusetts’ “big data” industry meet its growing demand for well-trained data scientists, promote economic development in the Pioneer Valley and beyond, support a thriving community of working data scientists in Western Massachusetts, help expand the university’s R&D base, and solidify the campus’s future as a destination and partner-of-choice for basic as well as translational research in data science and artificial intelligence.

Over 45 faculty members within CICS are affiliated with the Center for Data Science and Artificial Intelligence. These scientists and their students develop and apply innovative methods to collect, curate, and analyze large-scale data, and significantly contribute to the capacity of public sector agencies, private sector companies, and social good organizations to make data-informed discoveries and decisions. Center for Data Science and Artificial Intelligence-led research spans diverse disciplines within the broader fields of computer science and informatics, including these:

• Artificial intelligence
• Machine learning
• Machine vision
• Knowledge representation and reasoning
• Natural language understanding
• Autonomous systems and robotics
• Game theory
• Advanced algorithms
• Computing at massive scale
• Equity, accountability, transparency and explainability

Center for Data Science and Artificial Intelligence researchers are applying data science and artificial intelligence methods in a wide variety of domains including health and biomedicine, digital humanities, ecology, food/water/energy systems, information retrieval, predicting and forecasting, and self-driving vehicles. The central intellectual challenges involve accessing, organizing, and deriving new knowledge from datasets that are massive in volume, variety, and rate of change over time. These challenges have evolved and will continue to do so in the years ahead as increasing numbers of organizations of all types collect and store increasing amounts of data of all types, and make those data readily available to algorithmic analysis.